import pytest
import httpx

from fastapi_modules.fastapi_leaudit.leaudit_bridge.resilient_clients import ResilientQwenVLMClient
from fastapi_modules.fastapi_leaudit.services.impl.pageQualityServiceImpl import PageQualityServiceImpl


class _FakeVlmClient:
    def __init__(self, response):
        self.response = response
        self.prompts = []

    async def extract_multifield(self, *, prompt, images_data_urls, max_tokens=800):
        self.prompts.append((prompt, images_data_urls, max_tokens))
        if isinstance(self.response, Exception):
            raise self.response
        return self.response


@pytest.mark.asyncio
async def test_vlm_page_quality_reject_result_is_used():
    service = PageQualityServiceImpl()
    service.VlmClient = _FakeVlmClient(
        {
            "status": "reject",
            "score": 0.18,
            "reason": "页面文字严重模糊，无法稳定辨认关键内容",
        }
    )

    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")

    assert status == "reject"
    assert score == 0.18
    assert "严重模糊" in reason
    assert "只输出 JSON" in service.VlmClient.prompts[0][0]
    assert "内嵌照片" in service.VlmClient.prompts[0][0]
    assert "即使页面周边文字清楚" in service.VlmClient.prompts[0][0]


@pytest.mark.asyncio
async def test_vlm_page_quality_embedded_evidence_blur_cannot_pass():
    service = PageQualityServiceImpl()
    service.VlmClient = _FakeVlmClient(
        {
            "quality_status": "疑似模糊",
            "quality_score": "0.42",
            "message": "内嵌证据照片主体发虚，门头文字不易辨认",
        }
    )

    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")

    assert status == "review"
    assert score == 0.42
    assert "内嵌证据照片" in reason


@pytest.mark.asyncio
async def test_vlm_page_quality_chinese_reject_status_is_supported():
    service = PageQualityServiceImpl()
    service.VlmClient = _FakeVlmClient(
        {
            "result": "不通过",
            "confidence": 0.1,
            "detail": "证据照片严重模糊，关键场所无法辨认",
        }
    )

    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")

    assert status == "reject"
    assert score == 0.1
    assert "严重模糊" in reason


@pytest.mark.asyncio
async def test_vlm_page_quality_json_string_result_is_supported():
    service = PageQualityServiceImpl()
    service.VlmClient = _FakeVlmClient(
        '{"status":"review","score":0.33,"reason":"页面内照片模糊"}'
    )

    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")

    assert status == "review"
    assert score == 0.33
    assert reason == "页面内照片模糊"


@pytest.mark.asyncio
async def test_vlm_page_quality_invalid_result_falls_back_to_review_not_pass():
    service = PageQualityServiceImpl()
    service.VlmClient = _FakeVlmClient({"status": "unknown", "reason": ""})

    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")

    assert status == "review"
    assert score == 0.5
    assert "VLM返回结果不可用" in reason


@pytest.mark.asyncio
async def test_vlm_page_quality_error_falls_back_to_review_not_pass():
    service = PageQualityServiceImpl()
    service.VlmClient = _FakeVlmClient(RuntimeError("vlm down"))

    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")

    assert status == "review"
    assert score == 0.5
    assert "VLM图片质量检测失败" in reason


@pytest.mark.asyncio
async def test_resilient_vlm_extract_multifield_keeps_raw_text_when_json_parse_fails(monkeypatch):
    client = ResilientQwenVLMClient(base_url="http://example.test", api_key="x", model="vlm-test")

    async def fake_post_with_retry(payload):
        return httpx.Response(
            200,
            json={
                "choices": [
                    {
                        "message": {
                            "content": "疑似模糊：内嵌证据照片主体发虚，建议人工复核",
                        }
                    }
                ]
            },
        )

    monkeypatch.setattr(client, "_post_with_retry", fake_post_with_retry)

    result = await client.extract_multifield(
        prompt="图片质量检测",
        images_data_urls=["data:image/png;base64,xxx"],
    )

    assert result["result"].startswith("疑似模糊")
    assert "内嵌证据照片" in result["reason"]